The use of artificial intelligence inside companies sped up in 2026. A survey by Luria, published by the Brazilian outlet Olhar Digital on October 7, shows that the number of people using AI at work grew 183% between January and August, compared with the same period in 2025. In addition, the monthly volume of queries rose 240%. However, these numbers come from large companies in the energy, pharmaceutical, financial and retail sectors. So the question for the owner of a smaller business is a different one: can you use AI for small businesses without an IT team and without a multinational budget?
You can, as long as AI comes in through a concrete process and not through a trendy tool. In this guide, you will see where AI already solves real problems in small companies, how to choose the first process, what it usually costs and which mistakes kill the project in the second month.
What the 2026 numbers show (and what they don’t)
The Luria data shows that, in companies that have already adopted AI, its use has stopped being an experiment and become routine. According to the survey, half of the users make three or more queries on each day they use the tool, and the peaks happen at 11 a.m. and 3 p.m., that is, in the middle of the workday and not in “testing” moments.
On the other hand, the study itself warns that it is not a survey of AI adoption in Brazil as a whole. It measures the clients of a corporate platform. Likewise, data from the TIC Empresas 2025 survey by Cetic.br, cited in the article, indicate that half of Brazil’s large companies used AI in 2025. In other words, the sample is always large companies.
For a small business, this can be read in two ways. First, the productivity gap between those who use AI and those who don’t is widening. Second, there is still room to get ahead of local competitors, because most small businesses still use AI loosely, without connecting the tool to any process.
Where AI for small businesses solves real problems
AI pays off most where there are repetitive tasks, steady volume and fairly clear rules. In practice, four areas concentrate the fastest gains.
Customer service and WhatsApp
In many small companies, WhatsApp is the main sales channel, and also the biggest bottleneck. Customers ask about price, lead time and availability, but the answer depends on someone who is busy with something else. An AI assistant can answer frequently asked questions, qualify the request and hand over to a human only what needs a decision. If the topic is still new to you, it is worth first understanding what chatbots are and how an AI chatbot works (article in Portuguese).
Quotes and proposals
Putting together a quote usually takes hours every week, since it involves copying data from spreadsheets, checking prices and formatting documents. With AI, a request that arrives by email or message can be read automatically, the items are matched against the price list and the draft comes out ready for review. That way, the salesperson only checks and sends it.
Documents and data entry
Invoices, receipts, PDF orders and contracts are documents that someone has to read and type into another system. This is one of the most mature uses of AI today: extracting the data from the document and entering it into the ERP or the spreadsheet, with human review only in doubtful cases.
Marketing and content
This is the most common use, and also the one with the smallest impact on its own. Generating post ideas, captions and emails saves time, but it rarely changes the company’s results by itself. So it is only worth starting here if content production really is the business’s bottleneck.
Where to start: a 30-day plan
The most common mistake is to start with the question “what is the best AI?”. As Allan Miranda, CEO of Luria, told the reporters: “The question that separates companies today is not which AI model they chose, because that changes every six months.” Instead, start with the process.
Week 1: choose a process and measure it
List the tasks your team repeats every week and choose one with volume and clear rules. Then measure how much time it takes today, for example “we answer 60 quote requests a week and each one takes 15 minutes”. Without that number, you will not know whether the AI worked.
Week 2: test with off-the-shelf tools
Before hiring any project, test the process with a general-purpose tool such as ChatGPT, Gemini or Claude, using real cases (without sensitive customer data). This way you quickly find out whether the AI understands the kind of request your business gets and where it makes mistakes.
Week 3: connect it to the workflow
This is where most companies stop, and it is where the gain is. An AI that lives in a browser tab depends on someone copying and pasting. An AI connected to WhatsApp, email, the spreadsheet or the ERP, on the other hand, works on its own and only calls the team when needed. This is the kind of AI automation project that makes a difference to cash flow, because it takes the task out of the team’s hands instead of just speeding it up.
Week 4: compare and decide
Finally, compare the time spent before and after, the error rate and how customers reacted. If the gain is clear, expand to the next process. If it isn’t, adjust or switch processes, because the problem may lie in the choice of task and not in the technology.
How much it costs to use AI in a small business
The cost depends much more on the level of integration than on the tool itself. Broadly speaking, there are three levels:
- General-purpose tools: individual plans for AI assistants, paid per user, are useful for testing and for one-off tasks.
- Off-the-shelf tools with built-in AI: CRMs, customer service platforms and management systems that already include AI features, usually in higher-tier plans.
- Custom automation: AI connected to your systems and your process, with an implementation cost and a monthly fee for model usage, which is usually proportional to volume.
To decide, the simplest calculation is to compare the monthly cost of the solution with the hours it frees up. If an automation gives back 40 hours a month of one team member’s time, the value of those hours is the ceiling of what makes sense to pay.
Common mistakes when adopting AI in small companies
Some mistakes happen so often that it is worth knowing them before you start.
Starting with the tool instead of the problem
Subscribing to a tool because “everyone is using it” creates cost without results. After all, without a chosen process and a starting number, there is no way to measure the return.
Not recording what the AI learns
This is the point raised by Luria: treating every conversation with the AI as disposable makes the company start from scratch every time the model changes. In other words, instructions, examples and business rules need to be documented and kept inside the company, not scattered across each employee’s personal history. It is the same risk we discussed in vendor lock-in and supplier dependence (article in Portuguese).
Sharing sensitive data carelessly
Customer data, contracts and financial information should not go into free tools without checking their privacy policy. In addition, data protection laws such as the GDPR or Brazil’s LGPD still apply, so processing this data requires a legal basis, whether or not there is AI involved.
Taking humans out of the loop too early
AI makes mistakes, and the mistakes look convincing. Therefore, at first, keep a person reviewing the answers and the entries. Later, with a track record of correct results, limit the review to exceptions.
Will AI replace a small business’s team?
In most cases, no. What the data shows so far is AI taking over tasks, not entire jobs. We discussed this with numbers in AI and jobs: what the real data shows (article in Portuguese). In a small company, where each person wears several hats, the most common effect is freeing the team from typing and repetitive answers so they can take care of customers and sales.
Conclusion
AI for small businesses is no longer a topic for the future. While large companies nearly tripled their use in a year, small businesses have the advantage of deciding quickly and testing in weeks. In short, the path is to choose a repetitive process, measure it, test with off-the-shelf tools, connect it to the real workflow and only then expand. That way, technology turns into results, and not into yet another forgotten subscription.
If you want to know which process in your company would give the best return with AI, Agência Byte runs this assessment with you and shows, on your real workflow, how the automation would work.